Method and system for processing signals via perceptive vectorial quantization, computer program product therefore

ABSTRACT

The system carries out conversion of digital video signals organized in blocks of pixels from a first format to a second format. The second format is a format compressed via vector quantization. The vector quantization is performed by means of repeated application of a scalar quantizer to the pixels of said blocks with a quantization step (Q) determined in an adaptive way according to the characteristics of sharpness and/or brightness of the pixels.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present disclosure relates generally to techniques for signalprocessing and has been developed with particular but not exclusiveattention paid to possible applications in the framework of systemswhich envisage reduction in the quantity of data required forrepresenting, in a digital format, an image (still picture) or asequence of images (video sequence).

[0003] 2. Description of the Related Art

[0004] Known to the art are various solutions for efficient compressionof digital images. These solutions are usually characterized by a highcomputational complexity and are not easily integratable in thesolutions commonly referred to as System on a Chip (SoC).

[0005] The techniques of compression of digital images can be classifiedin two fundamental groups.

[0006] A first group comprises the so-called lossless compressiontechniques i.e., techniques without loss of quality, which can be usedalso for processing other types of digital data. The purpose of thistype of compression is to remove the statistical redundancy of the data.

[0007] To each digital datum there is assigned a variable number ofbits, which depends upon the statistical frequency of the particulardatum in question.

[0008] With reference, by way of example, to the so-called Huffmanncompression, to each digital datum there is assigned a variable integernumber of bits according to the following rule: short binary codes areassigned to the more frequent data, whereas long binary codes areassigned to less frequent data.

[0009] Also known are techniques of arithmetic compression, in which toeach digital datum there is assigned a variable and fractional number ofbits. The criterion of assignment of the bits is similar to the one usedfor the Huffmann compression.

[0010] Other compression methods are based upon the use of dictionaries.The sequences of the digital data to be compressed are reduced to wordsof variable length of a dictionary. Corresponding to each word is anappropriate binary code of a fixed or variable length. Belonging in thiscontext is the algorithm for identification of the optimal dictionarydue to Lempel and Ziv.

[0011] A second group of known compression techniques comprises thelossy compression techniques i.e., techniques with loss of quality.

[0012] The purpose of this type of compression is to remove theperceptive redundancy in the data. The image is modified by eliminatingwhat cannot be perceived, or is perceived less, by the human visualsystem (HVS). The characteristic that is most widely exploited by thevisual system amounts to the fact that the sensitivity to lowfrequencies is higher than the sensitivity to high frequencies. Inaddition, the perception of the spatial resolution of brightnessinformation is more marked than the perception of chromaticityinformation.

[0013] The representation of the chromaticity information may thereforebe less precise, in the sense that the spatial resolution may be lower.The chrominance is, therefore, under-sampled as compared with thebrightness. The loss of quality which derives therefrom is practicallynot perceived by the human eye.

[0014] By way of example, for the ITU-R BT.601 standard, theunder-sampling ratio between the luminance signal (Y) and the two colordifferences (CbCr or UV or IQ or DbDr) is 4:2:2. For the well-known MPEGstandard the ratio is 4:2:0, where 0 indicates that under-sampling isboth vertical and horizontal.

[0015] Likewise, the representation of the other sequences may be lessprecise, in the sense of a coarser quantization, with consequent savingin bits. The loss of perceived quality that derives therefrom is,however, low on account of the lower sensitivity of the visual system tothese frequencies.

[0016] The splitting into high and low frequencies can be done onlyafter having passed from the spatial domain to the frequency domain bymeans of the transformation operation. The most widely usedtransformations are, by way of example, the discrete cosine transform(DCT) and the discrete wavelet transform (DWT).

BRIEF SUMMARY OF THE INVENTION

[0017] One embodiment of the present invention provides a solution thatis improved as compared to the ones provided by the known art fromvarious standpoints and, in particular, as regards the needs to keepcomputational complexity (i.e., the number of processing operations andthe number of storage units) low with a view to integration of thefunction of image compression or decompression in a more complex system,without penalizing markedly compression efficiency and, therefore,enabling a reduction in the area occupied on the silicon by thecorresponding circuits, with the consequent reduction in productioncosts, at the same time achieving a reduced dissipation (this latterfactor being particularly important for low-power devices).

[0018] Another embodiment of the invention also regards thecorresponding system, as well as the corresponding computer product,which is directly loadable into the internal memory of a digitalprocessor and contains portions of software code that are able toimplement the process when said computer product is run on a numericprocessor.

[0019] The solution according to an embodiment of the invention can beintegrated, for example, both in a digital unit for image acquisition(CCD/CMOS sensors) and in an image-display unit (LCD display). Inparticular, in the case of igital cameras and similar or relateddevices, the data are acquired by the sensor according to a spatialpattern known as Bayer color-filter array (CFA), which enablesassociation of just one of the three color components to each pixel. Thecorresponding RGB image is then reconstructed by means of animage-processing sequence (image-generation pipeline, IGB), among whichthere is always present a block dedicated to compression.

[0020] The fact of anticipating this step, placing it immediately afterthe acquisition of the data from the sensor, enables a reduction of theband necessary for transmission of the image from the processing unit orstorage unit to the display. This solution is useful above all in thecase of applications in which the acquired data are to be transmittedfor remote processing thereof.

[0021] Basically, the solution according to an embodiment of theinvention is based upon the construction of a vector ormulti-dimensional quantizer with non-uniform quantization cells fordigital-data arrays containing (linear or non-linear) chromaticcomponents.

[0022] The vector quantizer is built so as to enable the simultaneousreduction of the statistical and perceptive redundancy of the datacontained in the array and to minimize the complexity of the encoding(which corresponds to compression) and the decoding (which correspondsto decompression).

BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Embodiments of the invention will now be described, purely by wayof non-limiting examples, with reference to the annexed drawings, inwhich:

[0024]FIG. 1 illustrates, in the form of a block diagram, the scheme ofan encoding circuit operating according to an embodiment of theinvention;

[0025]FIG. 2 illustrates, once again in the form of a block diagram, thestructure of an embodiment of a decoding circuit which can be used inthe context of the invention;

[0026]FIG. 3 illustrates the scanning diagram of one of the chromaticcomponents (in particular, the green component) of a so-called Bayerpattern, in the context of a system operating according to an embodimentof the invention; and

[0027] FIGS. 4 to 7 represent various examples of application of thesolution according to embodiments of the invention.

DETAILED DESCRIPTION

[0028] Embodiments for processing signals via perceptive vectorialquantization are described herein. In the following description,numerous specific details are given to provide a thorough understandingof embodiments of the invention. One skilled in the relevant art willrecognize, however, that the invention can be practiced without one ormore of the specific details, or with other methods, components,materials, etc. In other instances, well-known structures, materials, oroperations are not shown or described in detail to avoid obscuringaspects of the invention.

[0029] Reference throughout this specification to “one embodiment” or“an embodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present invention. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment” invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

[0030] With initial reference to the diagram of FIG. 1, the blockdesignated by 10 represents, as a whole, a sensor (in what follows, thesensor in question will be assumed to be an image sensor of the typecommonly referred to as Bayer sensor), which is able to generate atoutput a signal x(i) representing an image in a digital format.

[0031] Sensors of the above type are widely known to the art; therefore,the corresponding operating characteristics, also as regards thecharacteristics of the signals generated, do not call for a detaileddescription herein.

[0032] The reference number 11 designates, in FIG. 1, a predictor block,which can generate, from the input image generated by the block 10, acorresponding prediction signal p(i). In the example illustrated herein,the prediction signal is generated, for each signal x(i), as a functionof the previous value x(i-1).

[0033] Consequently, the signal p(i) can be expressed as:

p(i)=predictor (x(i))=x(i−1).

[0034] The signals x(i) and p(i) are then added (with opposite signs) ina summation node 12, which generates, at its output, a prediction-errorsignal e(i) that can be expressed in general as:

e(i)=x(i)−p(i)

[0035] with e(1)=x(1).

[0036] The reference 13 designates a block in which the prediction-errorsignal e(i) undergoes quantization and is then subjected, in a blockdesignated by 14, to a symbol-encoding function which lies at the basisof the output image, designated, as a whole, by 15.

[0037] The encoding diagram represented, merely by way of example andhence without any intention of limiting the scope of the invention, inFIG. 1 is, therefore, basically of the type commonly referred to asdifferential PCM (DPCM). The lossy aspect of the encoding is clearlylinked to the quantization function implemented in block 13.

[0038] The dashed line designated by L indicates that, in the case of alossless encoding, the prediction errors are not quantized before theencoding carried out in block 14.

[0039] The block diagram of FIG. 2 illustrates the complementarydecoding function. Here, the input image 25 (comprising a compressedimage which can be virtually identified with the image 15 deriving fromthe encoding action) is supplied to a symbol-(de)coding block 26 andthen passes to a (de)quantization block 27 where the prediction-errorsignal e(i) is reconstructed and is then added with sign, in a nodedesignated by 28, to a prediction signal p(i) generated by a predictionblock 29. The above is done in order to generate a signal correspondingto the reconstructed output image represented by block 30.

[0040] It will be appreciated that, also in the case of the DPCM decoderof FIG. 2, the prediction block 29 operates according to the value ofthe reconstructed signal for the preceding instant in time.

[0041] In other words, the decoding function represented in the diagramof FIG. 2 corresponds to the implementation of the relation:

x(i)=e(i)+p(i)

[0042] with x(1)=e(1).

[0043] Also in the diagram of the decoder illustrated in FIG. 2, thedashed line designated by L shows the possible recourse to losslessdecoding techniques, in which the signal at output from the block 26 issent immediately downstream, by-passing the dequantization block 27.

[0044] The diagrams represented in FIGS. 1 and 2 have an altogethergeneral and generic nature and apply to digital image signals of anykind.

[0045] The quantization operation represented by block 13 (of course,the same considerations apply, in a complementary way, to thedequantization block 27) envisages that the array of data representingthe input signal in the specific case, the prediction error e(i) will besplit into blocks of pixels. When the block of pixels contains just onepixel, the quantization technique deriving therefrom is referred to asscalar quantization; otherwise, it is referred to as vector ormulti-dimensional quantization.

[0046] In order to obtain minimum computational complexity, two pixelsper block are preferably chosen. In this case, the quantizer istwo-dimensional, i.e., the vector to be quantized has a dimension of 2.

[0047] When the number of pixels so enables, the block is preferablysquare, the aim being not to favor vertical orientation over horizontalorientation, in order to increase compression isotropy.

[0048] In the case of image sequences, the three-dimensional block ispreferably cubic, for the reasons already illustrated.

[0049] The intensity of the pixels contained in the block constitutes ann-tuple of co-ordinates in an n-dimensional space. The n-dimensionalspace is partitioned into cells, each cell containing a reconstructionpoint. To each reconstruction point there is assigned an appropriatebinary code.

[0050] The vector or multi-dimensional quantization operation, which canbe implemented in block 13, comprises passing from the binaryrepresentation of the intensity of the pixels contained in the block tothe binary code assigned to the reconstruction point of the cell,selected on the basis of the pixels themselves.

[0051] The simplest vector quantizer is the one comprising a scalarquantizer applied n times to each pixel of the block. The vectorquantizer represented here basically comprises a scalar quantizerapplied to each pixel in the block with a quantization step calculatedin an adaptive way according to the characteristics of the pixelsthemselves.

[0052] The compression technique is, as has been seen, of a lossy type.In fact, the reconstruction point is generally coordinated and differentfrom the point corresponding to the n-tuple of starting co-ordinates.The difference is referred to as quantization error.

[0053] As will be illustrated in greater detail in what follows, byappropriately designing the vector quantizer, it is possible to obtainthe simultaneous reduction in the statistical redundancy and perceptiveredundancy of the data, at the same time maintaining a low computationalcomplexity.

[0054] A vector quantizer with uniform cells and variable-length codefor the reconstruction points achieves a reduction in the statisticalredundancy of the data. This is obtained by assigning short binary codesto the reconstruction points belonging to the cells selected morefrequently, in a manner similar to what is done in the Huffmanncompression technique.

[0055] The vector quantizer with non-uniform cells and fixed-length codefor the reconstruction points has asymptotically the same performance interms of reduction in statistical redundancy if it is built in anappropriate manner. In particular, the areas of the multidimensionalspace most frequently selected are partitioned with cells that aresmaller and nearer to each other.

[0056] The compression is due to the fact that the binary representationassociated to the reconstruction point requires fewer bits that thebinary representation of the elements of the vector to be quantized.

[0057] As regards the vector quantizer presented herein, afterexperimental measurements have been carried out, it is noted that, foran n-dimensional space, the cells must be concentrated along thestraight line of equation:

x ₁ =x ₂ =x ₃ = . . . x _(n)

[0058] where x_(i) is the i-th co-ordinate, with i ranging from 1 to n.It is, in practice, the n-dimensional diagonal.

[0059] The experimental measurements are justified by the fact that theoutlines or edges constitute a small part of the image. In the rest ofthe image, the intensity of the pixels of the block is approximatelyuniform, which means that in the multi-dimensional space thecorresponding co-ordinate is near the straight line represented by theabove equation.

[0060] It can moreover be noted that, whilst the number of the pixels ofthe image increases as N², i.e., as the square of the length of the side(if the image is assumed to be approximately square, with a side of Npixels), the number of pixels of the edges increases only according toN. On the basis of this observation, it is foreseeable that the vectorquantizer thus designed proves increasingly efficient as the imageresolution increases.

[0061] In other words, as the resolution increases, the percentage ofedges decreases. The bigger cells, which are far from the n-dimensionaldiagonal, with greater quantization error, are selected less on apercentage basis. It may be concluded that the signal-to-quantizationnoise ratio of the image increases.

[0062] On the basis of the premises recalled above, it is possible, oncemore with reference to a non-uniform vector quantization, to refer toreduction in perceptive redundancy.

[0063] As has already been said, the human visual system shows a lowsensitivity to the high frequencies contained in the image, which cantherefore be quantized in a coarser way.

[0064] It is possible to exploit in this way the said property of thevisual system by passing to the frequency domain by means of a (DCT orDWT) transform of a block of pixels. This operation usually provesrather burdensome in terms of computational complexity.

[0065] A simpler way to exploit said property is based upon operation inthe spatial domain instead of the frequency domain.

[0066] The above is based upon the fundamental idea of quantizing theimages in a coarser manner near the edges, where high frequencies arefound: it is, in practice, a masking effect which occurs at the edges.

[0067] Quantizing in a coarser way means increasing the quantizationstep. In effect, the quantization step can be the greater, the sharperthe edge, and the sharpness of the edge can be measured in differentways.

[0068] For example, if the block contains only two pixels (as in aminimum-complexity implementation) the sharpness of the edge is simplythe absolute difference of the light intensity of the two pixels. If theblock contains more than two pixels, a simple and rapid measurement maybe the absolute difference between the highest intensity and the lowestintensity present in the block.

[0069] After the sharpness of the edges has been calculated (accordingto known criteria, on the basis of the premises indicated above), it isquantized so as to divide the edges into a number of classes. Forexample, in the case of a block of two pixels, E=abs (P₁−p₂). If p₁ andp₂ range from 0 to 255, the sharpness of the edge E ranges from 0 to255. If E is quantized with a step E_(SQ)=max (E)/3, its quantized valueis E_(Q)=round (E/E_(SQ)), and four classes of edges are obtained, sinceE_(Q) ranges from 0 to 3.

[0070] Alternatively, it is possible to use a threshold table forclassifying E while assigning the values E_(Q) accordingly.

[0071] The class 0 corresponds to the least sharp edge (flat area), theclass 3 corresponds to the sharpest edge. The step of quantization ofthe pixels of the block is chosen the higher, the sharper is the edge.

[0072] The quantization step Q can be read from an indicized tableaccording to the edge class E_(Q). Alternatively, the quantization stepQ can be calculated as a function of the parameter E_(Q).

[0073] For example, we can simply set Q=m*E_(Q)+q with m and q constantvalues appropriately chosen. Alternatively, and in one embodiment, wecan set Q=m*(t{circumflex over ( )}E_(Q))+q. To maintain a lowcomputational complexity, q=0 and t=2.

[0074] More in general, and in an embodiment, the function which enablescalculation of Q from E_(Q) is defined so that the value of Q will bemultiplied by an integer value if E_(Q) increases and will be divided byan integer value if E_(Q) decreases. This makes it possible to simplifythe subsequent calculation of the binary code (block 14 of FIG. 1), tobe supplied at output, from the result of the vector quantization (block13 of FIG. 1).

[0075] The vector quantization is then reduced by repeatedly applying ascalar quantizer with a quantization step Q calculated in an adaptiveway in order to exploit the effect of masking of the edge. Inparticular, the intensity of each pixel of the block p_(i) is quantizedas follows: p_(iQ)=round (p_(i)/Q).

[0076] The reconstruction level (for inverting the quantizationoperation block 27 of FIG. 2) is simply set equal to p_(iR)=p_(iQ*Q.)

[0077] The set of quantized intensities p_(iQ) of the block is used forgaining access to a table in which a constant-length or variable-lengthbinary code, associated to the construction level previously identified,is specified. In an embodiment, said code is of constant length.

[0078] Alternatively, said binary code is derived via binary arithmeticoperations from the binary representations of the quantized blockintensities appropriately clustered.

[0079] It will be appreciated that the resulting vector quantizer hascells more concentrated along the n-dimensional diagonal. A simultaneousreduction in statistical redundancy and in perceptive redundancy is thusachieved.

[0080] A further property of the human visual system is the lowersensitivity as brightness increases. This property is already exploitedwell by the exponential relation which relates the linear chromaticcomponents to the corrected non-linear ones, with the use of theso-called gamma factor.

[0081] The corrected components are the ones normally used. It isexperimentally found that said corrected components still in partpresent the aforementioned perceptive redundancy.

[0082] In particular, the mean value M of brightness of the pixels inthe block is calculated. This level is quantized with a step M_(SQ) soas to divide the blocks into a number of classes: M_(Q)=round(M/M_(SQ)). If, for example, M_(SQ)=max (M)/2, MQ may assume the values0, 1, or 2.

[0083] Alternatively, it is possible to use a threshold table forappropriately classifying M, accordingly assigning the values M_(Q).

[0084] The quantization step Q may be increased or decreased by anamount proportional to M_(Q) via an appropriate constant, thusexploiting the effect of masking of the high light intensity.

[0085] Alternatively, it is possible to use a table to derive Q fromM_(Q). It is also possible to calculate Q directly from E_(Q) and M_(Q)taken together by means of an appropriate formula or table, the latterusually implemented in the form of a so-called look-up table (LUT).

[0086] As has been mentioned previously, the function that enablescalculation of Q from E_(Q) is preferably such that the value of Q willbe multiplied by an integer value if M_(Q) increases and will be dividedby an integer value if M_(Q) decreases.

[0087] The resulting vector quantizer, derived from the previous one,has cells more concentrated along the n-dimensional diagonal (x₁=X₂=X₃=.=Xn). In particular, the concentration is higher at the beginning of thediagonal (x₁=X₂=X₃=. =Xn=c, with c small) and lower at the end(x₁=x₂=x₃=. =xn=c, withclarge).

[0088] The vector quantizer thus far described consists in a scalarquantizer applied to each element of the vector to be quantized. Thequantization step is identical for all the elements of the vector and iscalculated according to the perceptive characteristics of the vectoritself: sharpness of the edge, if present, and mean brightness.

[0089] The reconstruction points of this vector quantizer are arrangedaccording to an orthogonal lattice, having square cells, in thetwo-dimensional case, or cubic cells, in the three-dimensional case.

[0090] For the two-dimensional case, it is known that the optimallattice, with cells all the same as one another, is the one withhexagonal cells. The reason is that the maximum quantization error isdue to the point which, in the cell, is further away from thereconstruction point. The ideal cell is consequently the circular cell,and the hexagonal cell is the one that best approximates the ideal cellwhilst covering completely the space to be quantized.

[0091] The quantizer with hexagonal cells can be obtained from aquantizer with rectangular cells, in which the reconstruction pointshave co-ordinates that increase with a step DX=(3/2)*L, DY=sin (π/3)*L,with pre-set L calculated in the way already described for thequantization step. The reconstruction points of the rectangular latticehave co-ordinates X=n*DX, Y=m*DY, with n and m integers. Thereconstruction points of the hexagonal lattice are a sub-set of theseand precisely are the points where o=m+n is even (or else odd).

[0092] Alternatively, and in one embodiment, the cell is square. In thiscase DX=DY=L, with pre-set L calculated in the way already described forthe quantization step. If only the reconstruction points where o=m+n iseven (or else odd) are considered, a square lattice rotated through 45°is obtained, hence a lattice which basically amounts to a quincunxconfiguration.

[0093] Alternatively, it is possible to use a lattice with hexagonalcells rotated through 45°, in order to align one of the borders of thecells to the n-dimensional diagonal according to which the space to bequantized is partitioned.

[0094] The vector quantizer thus far described obtained with a scalarquantizer applied n times (one for each pixel of the block of theimage), for which the quantization step Q is calculated in an adaptiveway according to the sharpness E of the edge present in the block andaccording to the mean light intensity M thereof. In this way, thesimultaneous reduction of the statistical and perceptive redundancy isobtained.

[0095] Such a quantizer can be applied to pixel arrays corresponding tothe luminance, the chrominance, or to a given color (R, G, or B).

[0096] In the case of chromatic components under-sampled (as in the caseof video signals in the formats YUV, YIQ or YDbDr in the 4:2:2 format)and multiplexed (as in the case of the digital video signal YCbCr ITU-RBT.601), the block of pixels must be appropriately treated, re-orderingand demultiplexing the components to which the vector quantizer is to beapplied.

[0097] For example, considering a pair of 8-bit pixels corresponding tothe same chromatic component, this is replaced by an 8-bit index whichidentifies one of the reconstruction points which are concentrated aboutthe two-dimensional diagonal. The compression factor is, in this case,2:1. It is evident that, if 8 bits are used, i.e., 2⁸=256 cells in thetwo-dimensional space corresponding to each pair of pixels. A particularcase is represented by data organized according to a so-called Bayerpattern (see in this regard FIG. 3, which refers to a scanning diagramof the green component of the Bayer pattern for an adaptive DPCMencoding/decoding system).

[0098] The data in Bayer format obviously represent an approximation ofthe chromatic components of a scene that can be acquired by a digitalsensor. The final quality of the image is strictly linked to the colorreconstruction/interpolation algorithms. When the aim is to implement acompression function it is, however, important, in the case of a lossycompression, to attempt to preserve a high fidelity with respect to theoriginal data. Small alterations could, in fact, alter/worsen thequality of the final RGB image with effects such as false colors, aso-called diffused aliasing, etc. It is thus important to use techniquesthat take into account the particular structure, exploiting preciselythe chromatic correlation of the different channels.

[0099] Following, in fact, a global approach of a traditional type (forexample, JPEG), the transitions between pixels of different colors wouldbe encoded as high-frequency and consequently markedly alteredcomponents. On the other hand, in the case of low-cost applications, thetechnique must necessarily exploit just a few computational resources,remote reconstruction being envisaged.

[0100] In the case of an array containing a Bayer pattern, the block ofdimensions 4×2 contains the following scheme of chromatic components:

[0101] row 1=G₁R₁G₂R₂,

[0102] row 2=B₁G₃B₂G₄; using the two-dimensional vector quantizer, thepairs to which said quantizer can be applied are <R₁, R₂>, <B₁, B₂>together with <G₁, G₂>, <G₃, G₄> or <G₁, G₃>, <G₂, G₄>.

[0103] It is experimentally observed that, if<G₁, G₂>, <G₃, G₄> arevector quantized, the quality improves. In fact, in the other case, thepixels that belong to a pair are more distant from one another, and itis less likely for them to have similar intensities, i.e., it is lesslikely that the point of corresponding co-ordinates is near the diagonalwhere the quantization is finer and the error smaller.

[0104] In the case of images in RGB format, it is usually convenient toperform a change of co-ordinates to the color space YCbCr (or else toany of the similar spaces in which the luminance information is separatefrom the chrominance information, i.e., YUV, YIQ or YDbDr).

[0105] For a general review of the characteristics of said chromaticspaces, as well as the other chromatic spaces to which reference is madein the framework of the present description, useful reference may bemade to the following documents:

[0106] R. C. Gonzales, R. E. Woods, Digital Image Processing, AddisonWesley, 1993;

[0107] W. B. Pennebaker, J. L. Mitchell, JPEG, still image datacompression standard, Van Nostrand Reinhold, 1992; and

[0108] D. Taubman, M. Marcellin, JPEG2000Image Compression Fundamentals,The Kluwer Int. Series in Eng. and Computer Science, Volume 642Hardbound, ISBN 0-7923-7519-X, November 2001).

[0109] The chrominance planes are then sub-sampled horizontally (4:2:2format), or else both horizontally and vertically (4:2:0 format).Possibly, the operation of sub-sampling can be preceded by a low-passfiltering for reducing the aliasing effects, above all in the case ofnon-progressive multiplexed video material.

[0110] The luminance plane is then compressed by applying thetwo-dimensional vector quantizer to (horizontally or vertically)adjacent pairs of pixels. For example, from row=Y₁Y₂Y₃Y₄, we move ontothe pairs <Y₁, Y₂> and <Y₃, Y₄>, which are then vector quantized. Thechrominance planes are processed in a similar way but separately.Alternatively, and in another embodiment, sub-sampling of thechrominance planes is performed according to a quincunx (i.e.,checkerboard) configuration, proceeding so that the two chrominanceplanes can be multiplexed perfectly: row 1=U₁V₁U₂V₂, row 2=V₃U₃V₄U₄. Thevector quantization is then to be applied to the pairs <U₁, U₃>, <U₂,U₄> and <V₁, V₃>, <V₂, V₄>.

[0111] Alternatively, but with a slightly lower quality, the followingpairs can be used: <U₁, U₂>, <U₃, U₄> and <V₁, V₂>, <V₃, V₄>. The poorerquality is due to the greater spatial distance between the pixels of thepairs, which renders more likely a lower correlation. The pair to bevector quantized is consequently located far away from themulti-dimensional diagonal and is quantized with a higher quantizationerror.

[0112] The above checkerboard sub-sampling lattice proves to be moreisotropic as compared to the 4:2:2 case, in so far as it does not givepreference to the horizontal edges. In addition, perfect multiplexingbetween the components causes a chrominance component always to beassociated to each luminance pixel, instead of having alternately pixelsfor which the set YUV is specified and pixels for which only the value Yis specified. This enables a reduction of the artifacts due to thesubsequent step of interpolation and reconstruction of the original RGBimage.

[0113] As has been seen, vector quantization of multiplexed chromaticcomponents is obtained by grouping each component in the block into avector of appropriate size and quantizing it separately. In anembodiment, the vector has a minimum size of 2.

[0114] If the aim is to decompress the image, the binary code in thereconstruction point (supplied at output from block 26 of FIG. 2) mustbe replaced with the co-ordinates of the point itself. In the case ofsub-sampled and multiplexed chromatic components, it is then necessaryto separate the components (demultiplexing) and interpolate.

[0115] It is experimentally found that the vector-quantization errorgives rise to visible colored patterns. This occurs above all in theuniform areas of the image. The cause is to be chiefly attributed to theinterpolation method which necessarily makes use of adjacent pixels thatare of the same chromatic component but are affected by repeated andregular quantization error.

[0116] A possible solution of the problem involves trying to introducethe so-called dithering in order to brake the regularity of thequantization error.

[0117] In practice, a (low) level of noise is intentionally added so asto prevent the colored pattern. The disadvantage is that the quality ofthe image is slightly reduced in so far as the image appears slightlygranular.

[0118] A second possible solution involves applying noise-shapingtechniques, taking into account during quantization the previousquantization error. The disadvantage of this solution lies in the factthat the complexity of the quantizer increases (albeit slightly).

[0119] A particularly advantageous solution involves envisaging for theuniform areas (i.e., the areas with E=0) the finest possiblequantization with the minimum quantization step (Q=1).

[0120] For instance, developing the example seen previously (passagefrom RGB to modified YCbCr), for each pair <c₁, c₂>, if c₁=c₂=c (8bits), then the sequence of bits “1” plus 8 bits for c is sent;otherwise, if c₁ is other than c2, the sequence of bits “0” plus 8 bitscorresponding to the index VQ (c₁c₂) is sent. In all, we pass from the16 bits of the pair <c₁, c₂> to 8 bits.

[0121] The above solution falls, of course, within the vector quantizerscheme outlined previously. In fact, setting the quantization step tothe minimum (Q=1) for the uniform areas (where E=0) means that thereconstruction points of the vector quantizer, in addition to beingconcentrated near the n-dimensional diagonal, are also located on thediagonal itself. This is important for preventing visible coloredpatterns.

[0122] The above patterns belong, moreover, to the category of artifactsfor which the standard measurements of quality (such as the PSNR factor)do not correspond to the perceived quality. In fact, standardmeasurements of quality are based upon the intensity of the artifact anddo not take into account the influence thereof on the visual system. Inthe present case, the patterns in question have a low intensity but,since they are regular, they may appear clearly visible and henceperceivable by the user.

[0123] The vector-quantization or multidimensional-quantizationoperation described herein involves passing from the binaryrepresentation of the intensity of the pixels contained in the block tothe binary code assigned to the reconstruction point of the cellselected according to the pixels themselves.

[0124] From another point of view, it may be stated, with substantialadherence to the actual situation, that the block of pixels is encodedas a sort of single “superpixel” having an intensity specified by thebinary code associated to the selected reconstruction point.

[0125] Experimentally, a residual statistical redundancy of the data isfound, which can be further reduced in a lossy way by applying onceagain the solution presented herein or else in a lossless way byconcatenating one of the entropic-compression methods already presented.

[0126] The efficiency of the iterated lossy compression basicallydepends upon the way in which the binary codes are assigned to eachreconstruction point. In particular, considering two reconstructionpoints, the assignment must be made in such a way that corresponding toa smaller distance in the n-dimensional space there will be a smallerdifference in the related binary values. Assuming that such anassignment has been carried out, it is understandable that a superpixelhas a value similar to that of the adjacent ones, precisely on accountof the residual statistical redundancy. The superpixels can therefore beintroduced in multi-dimensional and quantized vectors with a proceduresimilar to the one already illustrated.

[0127] With reference once again to the example of the Bayer pattern, inthe case of multiplexed chromatic components we pass from a Bayerpattern to a super Bayer pattern. In fact, each 4×2 block of thestarting Bayer block (row 1=G₁R₁G₂R₃, row 2=B₁G₃B₂G₄) is encoded in a2×2 superpixel block (row 1=G′R*, row 2=B*G″), by means of thetwo-dimensional vector quantization (R*=VQ<R₁, R₂>, B*=VQ<B₁, B₂>,G′=VQ<G₁, G₂>, RG″=VQ<G₃, G₄>).

[0128] There is the evident possibility of iterating the method on thesuper Bayer pattern thus obtained. From the point of view of vectorquantization, this means increasing the size of the vector quantizer,since operation is carried out on blocks which refer to increasinglylarger portions of the original data array.

[0129] It is moreover evident that it is possible to iterate thecompression also in the case of the modified YCbCr.

[0130] To attain a further reduction of the residual statisticalredundancy it is possible to resort to an entropic encoding.

[0131] By way of example, the simplest method involves identifyingadjacent superpixels with the same value. This sequence of superpixelsis then reduced to a single sample of the superpixel, preceded orfollowed by a count indicating the number of repetitions. It is, inother words, an application of the technique known as run-lengthencoding (RLE).

[0132] The application of more advanced methods is more effective withan appropriate assignment of the binary codes and the reconstructionpoints (block 26 of FIG. 2). In particular, optimal assignment of thecodes follows the rule already illustrated previously.

[0133] Taking two reconstruction points, the assignment must beperformed so that corresponding to a smaller distance in then-dimensional space will be a smaller difference of the correspondingbinary values.

[0134] Assuming that such an assignment has been made, it may beappreciated that the value of a superpixel can be predicted according tothe value of the adjacent superpixels. In the simplest case, the valueof the superpixel is predicted according to the value of the precedingone. The prediction error is then encoded with a technique which canbasically be identified as a DPCM technique to which the diagrams ofFIGS. 1 and 2 refer.

[0135] At a distance from the edges (and, consequently, probably for themajority of the superpixels), the prediction error is small. Smallvalues of this error can then be classified with short binary codes,whereas large values will have long binary codes, in a way similar towhat has been seen in relation to Huffmann compression.

[0136] Of course, in the case of multiplexed chromatic components, thesimplest prediction of a given superpixel is made on the basis of thevalue of the nearest superpixel belonging to the same chromaticcomponent.

[0137] In more complex cases, instead, the prediction of a chromaticcomponent can be made on the basis of adjacent superpixels belonging toanother chromatic component (as is normally the case in the methods ofchromatic interpolation).

[0138] For example, in the particular case where the encoded data are inthe Bayer format, a slight modification in the prediction scheme of aDPCM type enables improvement of performance in the case of encoding ofthe green component.

[0139] In a Bayer pattern, in fact, the green pixels are present on eachrow, whilst the blue and the red ones are distributed on alternate rows.Consequently, the fact that continuous green pixels belonging tosuccessive rows are nearer in space than adjacent pixels on the same rowresults in a higher correlation, which, in turn, involves lowerprediction errors, at least in the case of areas of images in whichsharp edges are not present.

[0140] A prediction scheme following a “zigzag” order, in thecalculation of the errors, enables a slightly better compression ascompared with the classic scheme, both in the case of losslesscompression and in the case of lossy compression.

[0141] The degree of such an improvement (to which FIG. 3 makes specificreference) depends upon the characteristics of the image and increasesas the resolution increases.

[0142] Table 1 appearing below gives the mean results obtained ondatabases of images in Bayer pattern, which have different resolutionsand are compressed using both the adaptive DPCM-type approach, which hasjust been described (1-DPCM), and the classic approach (Standard DPCM orstd DPCM). Bit rate compression performance (expressed in bpp)Resolution algorithm lossless q = 2 q = 4 Q = 8 q = 16 q = 24 q = 32 352 × 288 std DPCM 4.79 3.83 2.96 2.19 1.65 1.45 1.33 I-DPCM 4.67 3.732.85 2.10 1.58 1.40 1.29  640 × 480 std DPCM 4.96 4.04 3.14 2.39 1.811.57 1.43 I-DPCM 4.87 3.95 3.06 2.31 1.71 1.54 1.40 1000 × 800 std DPCM3.57 2.74 2.07 1.62 1.34 1.24 1.18 I-DPCM 3.44 2.62 1.98 1.55 1.30 1.211.15

[0143] In the table, the value q designates the step used in the lossycompression obtained via uniform quantization.

[0144] Concatenating the lossless DPCM encoding system to the approachbased upon vector quantization, it is possible to obtain a bettercompression without any loss in quality of the output.

[0145] Although the vector quantizer operates satisfactorily with anytype of image from the perceptive point of view (subjective qualityevaluation), it is possible to improve the objective performance thereof(for instance, in terms of peak signal-to-noise ratio or PSNR) in thecase of images with sharp edges: a typical example is represented byimages created artificially on the computer, cartoons, text pagesintroduced via scanner or other means.

[0146] To obtain this improvement, the function that calculates thequantization step Q according to the sharpness of the edge E and of themean level of brightness M is modified. In particular, Q is chosen smallwhen E is maximum (sharp edge). In practice, Q is not simply made toincrease with E and M as seen previously but Q reaches a maximum at anintermediate value of E.

[0147] This means that the reconstruction points of the quantizationlattice are arranged in the corners far away from the multi-dimensionaldiagonal. These corners belong to the multi-dimensional cube, in whichthe vector that corresponds to the n-tuple of co-ordinates correspondingto the pixels in the block comes to be located.

[0148] Alternatively (and in addition to the strategy just illustratedfor the calculation of Q), it is possible to cause Q to be small whenone of the pixels in the block has the maximum or minimum allowablevalue. This means that the reconstruction points of the quantizationlattice are set not only on the corners distant from themulti-dimensional diagonal, but also along the sides of themulti-dimensional cube in which there the vector that corresponds to then-tuple of co-ordinates corresponding to the pixels in the block comesto be located.

[0149] The graphs of FIGS. 4a to 4 f reproduce examples ofreconstruction-point lattices and quantization cells for two-dimensionalvector quantizers. In particular, the graphs in question (the scales ofwhich, both on the abscissa and on the ordinate, are of themselvesirrelevant) refer respectively to a square lattice (FIGS. 4a, 4 b) andto a hexagonal lattice (FIG. 4c).

[0150]FIGS. 4d, 4 e and 4 f refer, instead, to error values foundrespectively with a Q scalar quantizer, a Q vector quantizer and anoptimal Q vector quantizer.

[0151] The images of FIGS. 5a to 5 d are demonstrate the performance ofa generic two-dimensional vector quantizer applied to 2×1 blocks of anarray containing luminance (compression factor 50%, from 8 bpp to 4 bpp)as compared with a scalar quantizer.

[0152] In particular, FIG. 5a illustrates an 8-bit/pixel image, whilstFIG. 5b shows the 2Q statistics for a 2×1 block.

[0153]FIG. 5c illustrates the distribution of the cells of thereconstruction points, whereas FIG. 5d illustrates the map of thequantization error.

[0154]FIGS. 5e and 5 f reproduce the cells and the reconstructionpoints, as well as the quantization error with reference to FIGS. 5g and5 h, which reproduce images on four bits per pixel obtained afterquantization with uniform 2Q quantizer and non-uniform 2Q quantizer.

[0155]FIGS. 6a to 61 demonstrate the performance of a two-dimensionalvector quantizer built according to the modalities described previouslyand applied to 4×2 blocks of an array containing chromatic componentsmultiplexed according to the Bayer pattern (compression factor 56%, from8 bpp to 4.5 bpp) as compared with a scalar quantizer and a vectorquantizer without points along the multi-dimensional diagonal.

[0156] In particular, it is assumed that the starting point is a24-bit/pixel RGB image (FIG. 6a), to which there corresponds the Bayerpattern on 8 bits/pixel reproduced in FIG. 6b.

[0157]FIG. 6c illustrates the RGB image reconstructed from the Bayerpattern, and FIG. 6d reproduces a part of the same image at an enlargedscale.

[0158]FIGS. 6e and 6 f illustrate, by way of comparison, the RGB imagereconstructed from a Bayer pattern compressed with a scalar quantizer,whereas FIGS. 6g and 6 h refer to the RGB image reconstructed from aBayer pattern compressed with a vector quantizer.

[0159]FIGS. 6i and 61 refer to an RGB image reconstructed from a Bayerpattern compressed with an optimal vector quantizer.

[0160]FIG. 7a shows the strong correlation between the G₁ (abscissa)component and the G₂ (ordinate) component of the Bayer pattern. It isobserved that the <G₁, G₁> pair is set along the two-dimensionaldiagonal of the quantization space and is quantized with a smallquantization error.

[0161] It appears clearly, instead, that this does not occur for the <G,R> pair (FIG. 7b), for the <G, B> pair (FIG. 7c) and for the <R, B> pair(FIG. 7d). For these pairs, the result of the perceptive vectorquantization would therefore be affected by a larger quantization error.

[0162] It is emphasized that, also in relation to FIGS. 7a to 7 d, asfor FIGS. 4a-f, 5 a-5 h, and 6 a-61, the exact definition of the scalesis not in itself relevant.

[0163] All of the above U.S. patents, U.S. patent applicationpublications, U.S. patent applications, foreign patents, foreign patentapplications and non-patent publications referred to in thisspecification and/or listed in the Application Data Sheet, areincorporated herein by reference, in their entirety.

[0164] Of course, without prejudice to the principle of the invention,the details of implementation and the embodiments may be amply variedwith respect to what is described and illustrated herein, withoutthereby departing from the scope of the present invention, as defined inthe claims that follow.

What is claimed is:
 1. A process for converting digital video signalsorganized in blocks of pixels between a first format and a secondformat, said second format being a format compressed via vectorquantization, the process comprising obtaining said vector quantizationfrom repeated application of a scalar quantizer to the pixels of saidblocks with a quantization step determined in an adaptive way accordingto characteristics of the pixels.
 2. The process according to claim 1wherein said quantization step is determined so as to increase accordingto a lack of uniformity of the pixels in the block.
 3. The processaccording to claim 2 wherein said quantization step is determined by alaw increasing according to multiples.
 4. The process according to claim1, further comprising detecting a level brightness of pixels in a blockand determining said quantization step in such a way that saidquantization step increases as a function of said level of brightness.5. The process according to claim 1, further comprising: detecting alack of uniformity of the pixels in a block; detecting a level ofbrightness of the pixels in the block; and determining said quantizationstep in such a way that said quantization step first increases and thendecreases as a function of said lack of uniformity and said level ofbrightness.
 6. The process according to claim 5 wherein saidquantization step is made to increase and decrease by multiples orsub-multiples.
 7. The process according to claim 4 wherein detecting thelevel of brightness of the pixels in the block is carried out bydetecting a mean level of brightness of the pixels in the block.
 8. Theprocess according to claim 1, wherein in passage from said first formatto said second format, said digital video signals are subjected to atleast one of the following: sub-sampling; low-pass filtering foranti-aliasing purposes before sub-sampling; and multiplexing of at leastone part of digital data necessary for representation of an image. 9.The process according to claim 1 wherein the digital video signalsinclude multiplexed chromatic components, wherein in passage from saidfirst format to said second format, the process further comprisingre-ordering the pixels in each block to be quantized by composing themin a vector such that the multiplexed chromatic components are quantizedseparately.
 10. The process according to claim 1, further comprisingidentifying, in a context of said digital video signals, blocks ofuniform pixels, choosing for said blocks of uniform pixels a minimumquantization step among quantization steps adopted in said vectorquantization.
 11. The process according to claim 1 wherein said digitalvideo signals in said second format are expressed in a form of binarycodes associated to respective quantized signals, the process furthercomprising executing a function of prediction of said binary codes. 12.The process according to claim 11 wherein said function of prediction ofthe binary codes is carried out according to a DPCM scheme.
 13. Theprocess according to claim 1 wherein in passage from said first formatto said second format, the signal compressed via vector quantization issubjected to entropic encoding.
 14. The process according to claim 13wherein said entropic encoding is performed with a technique chosen fromat least one of run-length encoding, Huffmann encoding, and arithmeticencoding.
 15. The process according to claim 1 wherein said vectorquantization is a multi-dimensional vector quantization resulting fromconcatenation of a plurality of vector quantizations, each resultingfrom repeated application of a scalar quantization.
 16. The processaccording to claim 15 wherein for each of said concatenated vectorquantizers, binary codes of reconstruction points are assigned in such away that the reconstruction points with small distance inmulti-dimensional space have binary codes with small difference.
 17. Theprocess according to claim 1 wherein said vector quantization isobtained with application of two scalar quantizers with quantizationsteps scaled by the constants 2/3 and sin (π/3).
 18. The processaccording to claim 17, further comprising defining points ofreconstruction allowed so that these points will form a hexagonallattice.
 19. The process according to claim 1, further comprising:identifying a value of edge sharpness in each of said blocks of pixels,dividing the edges into a number of classes; and attributing values tosaid quantization step differentiated according to the classes thusdetermined.
 20. The process according to claim 19 wherein saidquantization step is determined according to at least one law chosenfrom the following: m*E_(Q)+q; and m*(tAE_(Q))+q; where m and q areconstants, determined selectively, and E_(Q) is an index whichidentifies said edge class.
 21. The process according to claim 4,further comprising: dividing said level of brightness of the pixels inthe block in a number of classes; and selecting a value of saidquantization step in a different way according to the classes thusdetermined.
 22. The process according to claim 1 wherein said videosignals in said first format are signals generated according to aso-called Bayer pattern, which can be ordered in blocks of size 4×2having the following pattern of chromatic components: row 1=G₁R₁G₂R₂,row 2=B₁G₃B₂G₄; and wherein said vector quantization is atwo-dimensional vector quantization applied to pairs <R₁, R₂>, <B₁, B₂>together with <G₁, G₂>, <G₃, G₄> or <G₁, G₃>, <G₂, G₄>.
 23. The processaccording to claim 1 wherein said digital video signals in said firstformat are digital video signals in the RGB format and wherein saiddigital video signals in said second format are subjected to a change ofco-ordinates to a color space chosen from at least one of YCbCr, YUV,UIQ, and YDbDr.
 24. The process according to claim 1 wherein said vectorquantization is applied to adjacent pairs of pixels in a luminanceplane.
 25. The process according to claim 8 wherein in said digitalvideo signals in said second format, chrominance planes are sub-sampledaccording to a quincunx pattern.
 26. A system for converting digitalvideo signals organized in blocks of pixels between a first format and asecond format, said second format being a format compressed via vectorquantization, the system comprising at least one converter chosenbetween an encoder and a decoder and wherein said converter isconfigured for a vector quantization resulting from repeated applicationof a scalar quantizer to the pixels of said blocks with a quantizationstep determined in an adaptive way according to characteristics of thepixels.
 27. The system according to claim 26 wherein said converter isconfigured to determine said quantization step in such a way that saidquantization step increases according to a lack of uniformity of thepixels in a block.
 28. The system according to claim 27 wherein saidconverter is configured to determine said quantization step by a lawincreasing according to multiples.
 29. The system according to claim 26wherein said converter is configured to detect a level of brightness ofpixels in a block and determine said quantization step in such a waythat said quantization step grows as a function of said level ofbrightness.
 30. The system according to claim 26 wherein said converteris configured to: detect a lack of uniformity of the pixels in a block;detect a level of brightness of the pixels in the block; and determinesaid quantization step in such a way that said quantization step firstincreases and then decreases as a function of said lack of uniformityand said level of brightness.
 31. The system according to claim 30wherein said converter is configured to increase and decrease saidquantization step by multiples or sub-multiples.
 32. The systemaccording to claim 29 wherein said converter is configured to detect alevel of brightness of the pixels in the block by detecting a mean levelof brightness of the pixels in the block.
 33. The system according toclaim 26 wherein said converter is an encoder configured to subject saiddigital video signals to at least one operation chosen from:sub-sampling; low-pass filtering for anti-aliasing purposes beforesub-sampling; and multiplexing of at least one part of digital datanecessary for representation of an image.
 34. The system according toclaim 26 wherein the digital video signals comprise multiplexedchromatic components, wherein said encoder is configured to re-order thepixels in each block to be quantized by composing them in a vector suchthat the multiplexed chromatic components are quantized separately. 35.The system according to claim 26 wherein said encoder is configured toidentify, in a context of said digital video signals, blocks of uniformpixels and to choose for said blocks of uniform pixels a minimumquantization step among quantization steps adopted in said vectorquantization.
 36. The system according to claim 26 wherein saidconverter is configured in such a way that said digital video signals insaid second format are expressed in a form of binary codes associated torespective quantized signals and wherein said converter is configured toexecute a function of prediction of said binary codes.
 37. The systemaccording to claim 36 wherein said function of prediction of the binarycodes is carried out according to a DPCM scheme.
 38. The systemaccording to claim 26 wherein said converter is configured to subjectthe signals converted from said first format to said second format to afunction of entropic encoding or decoding.
 39. The system according toclaim 38 wherein said entropic encoding is performed with a techniquechosen from at least one of: run-length encoding, Huffmann encoding, andarithmetic encoding.
 40. The system according to claim 26 wherein saidconverter is configured for a vector quantization having amulti-dimensional vector quantization resulting from concatenation of aplurality of vector quantizations, each resulting from repeatedapplication of a scalar quantization.
 41. The system according to claim40 wherein said converter is configured to assign binary codes ofreconstruction points for each of said concatenated vector quantizationsin such a way that the reconstruction points with small distance in amulti-dimensional space have binary codes with small difference.
 42. Thesystem according to claim 26 wherein said converter is configured toobtain an application of two scalar quantizers with quantization stepsscaled by constants 2/3 and sin (π/3).
 43. The system according to claim42 wherein said converter is configured to define points ofreconstruction allowed so that these points will form a hexagonallattice.
 44. The system according to claim 26 wherein said converter isan encoder configured to: identify a value of edge sharpness in each ofsaid blocks of pixels, and to divide the edges into a number of classes;and attribute values to said quantization step differentiated accordingto the classes thus determined.
 45. The system according to claim 44wherein said converter is configured to determine said quantization step(O) according to at least one law chosen from: m*E_(Q)+q; andm*(tAE_(Q))+q; where m and q are constants, determined selectively andE_(Q) is an index which identifies said edge class.
 46. The systemaccording to claim 29 wherein said converter is configured to: dividesaid level of brightness of the pixels in the block into a number ofclasses; and select a value of said quantization step in adifferentiated way according to the classes thus determined.
 47. Thesystem according to claim 25 wherein said video signals in said firstformat are signals generated according to a so-called Bayer pattern,which can be ordered in blocks of size 4×2 having the following patternof chromatic components: row 1=G₁R₁G₂R₂ row 2=B₁G₃B₂G₄ and wherein saidvector quantization is a two-dimensional vector quantization applied topairs <R₁, R₂>, <B₁, B₂> together with <G₁, G₂>, <G₃, G₄> or <G₁, G₃>,<G₂, G₄>.
 48. The system according to claim 26 wherein said digitalvideo signals in said first format are digital video signals in an RGBformat and wherein said digital video signals in said second format aresubjected to a change of co-ordinates to a color space chosen from atleast one of YCbCr, YUV, UIQ, and YDbDr.
 49. The system according toclaim 26 wherein said vector quantizer is applied to adjacent pairs ofpixels in a luminance plane.
 50. The system according to claim 26wherein in said digital video signals in said second format, chrominanceplanes are sub-sampled according to a quincuncial pattern.
 51. Anarticle of manufacture, comprising: a machine-readable medium havinginstructions stored thereon to: convert a digital video signal organizedin blocks of pixels from a first format to a second format, includinginstructions to use vector quantization to compress the first formatinto the second format; and to obtain the vector quantization,repeatedly apply a scalar quantizer to the pixels of the blocks with anadaptive quantization step based on characteristics of the pixels. 52.The article of manufacture of claim 51 wherein the machine-readablemedium further includes instructions stored thereon to simultaneouslyreduce statistical and perceptive redundancy of data in the videosignal.
 53. The article of manufacture of claim 51 wherein themachine-readable medium further includes instructions stored thereon tochange the quantization step based on at least one of lack of uniformityof pixels in a block and a level of brightness of the pixels in theblock.
 54. The article of manufacture of claim 51 wherein the videosignals include multiplexed chromatic components, and wherein themachine-readable medium further includes instructions stored thereon toquantized the chromatic components separately.
 55. The article ofmanufacture of claim 51 wherein the machine-readable medium furtherincludes instructions stored thereon to concatenate a plurality ofvector quantizations to obtain a multi-dimensional vector quantization,each of the vector quantizations resulting from repeated application ofa scalar quantization.
 56. The article of manufacture of claim 55wherein the machine-readable medium further includes instructions storedthereon to: divide edges in each of the blocks of pixels into classes;and adapt the quantization step based on the classes.
 57. The article ofmanufacture of claim 51 wherein the machine-readable medium furtherincludes instructions stored thereon to: divide a level of pixels in theblocks into classes; and adapt the quantization step based on theclasses.
 58. A system, comprising: a means for receiving a digital videosignal organized into blocks of pixels at a first format and foroutputting the digital video signal at a second format; a means forencoding the digital video signal from the first format to the secondformat; and a means for performing vector quantization to compress thedigital video signal at the first format into the second format usingrepeated application of a scalar quantizer to the pixels of the blocks,including a means for adaptively determining a quantization step basedon characteristics of the pixels.
 59. The system of claim 58 wherein themeans for adaptively determining the quantization step include a meansfor changing the quantization step based on at least one of lack ofuniformity of pixels in a block, a level of brightness of pixels in theblock, and values of edge sharpness of an image.
 60. The system of claim58, further comprising a means for quantizing multiplexed chromaticcomponents of the digital video signal separately.
 61. The system ofclaim 58, further comprising a means for executing a function ofprediction of binary codes that are associated to respective quantizedsignals and that are used to express the digital video signals in thesecond format.
 62. The system of claim 58 wherein the means forperforming vector quantization include a means for concatenating aplurality of vector quantizations, each resulting from repeatedapplication of a scalar quantization.